on-policy-self-distillation

claude-opus-5

on-policy-self-distillation — graded by the task's verifier against a measured baseline.

backend: modal
variant: claude claude-opus-5
metric: AIME24 avg@12 accuracy
actions: 148
duration: 8h 50m
recorded: 2026-08-03

run idopsd-aime24-4gpus__g75tHnA

Everything claude-opus-5 did on this task, in order.

download JSON ↗

claude-codeclaude-opus-5138 turns34 narration148 action148 observation104 reasoning1 final

#101

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolEdit

args

{
  "replace_all": false,
  "file_path": "/workspace/repo/data_collator.py",
  "old_string": "PAD_SIDE = os.environ.get(\"OPSD_PAD_SIDE\", \"left\")",
  "new_string": "PAD_SIDE = os.environ.get(\"OPSD_PAD_SIDE\", \"right\")"
}

Edit result

The file /workspace/repo/data_collator.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/repo/data_collator.py", "oldString": "PAD_SIDE = os.environ.get(\"OPSD_PAD_SIDE\", \"left\")", "newString": "PAD_SIDE = os.environ.get(\"OPSD_PAD_SIDE\", \"right\")", "originalFile": null, "structuredPatch": [{"oldStart": 32, "oldLines": 7, "newStart": 32, "newLines": 7, "lines": [" TEACHER_CONTEXT = os.environ.get(\"OPSD_TEACHER_CONTEXT\", \"solution\")", " # \"left\" keeps the prompt block flush against the completion (see the padding note", " # in __init__); \"right\" reproduces the released recipe.", "-PAD_SIDE = os.environ.get(\"OPSD_PAD_SIDE\", \"left\")", "+PAD_SIDE = os.environ.get(\"OPSD_PAD_SIDE\", \"right\")", " ", " ", " class SelfDistillationDataCollator:"]}], "userModified": false, "replaceAll": false}

#102

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashMonitor cosine run

args

{
  "command": "sleep 115; sleep 115; sleep 115; sleep 115; python - <<'EOF'\nt=open('/workspace/runs_COS.log',errors='ignore').read()\nlines=[l.split('\\r')[-1] for l in t.split('\\n')]\nsel=[l for l in lines if \"'loss'\" in l or 'train_runtime' in l or 'OPSD kl' in l]\nprint('\\n'.join(l[:160] for l in sel[-4:]))\nEOF",
  "description": "Monitor cosine run",
  "timeout": 600000
}

Bash result

{'loss': -0.0079, 'grad_norm': 0.051960911601781845, 'learning_rate': 7.322330470336314e-07, 'on_policy_loss': -0.0079, 'epoch': 0.08}
{'loss': -0.0074, 'grad_norm': 0.0527726486325264, 'learning_rate': 6.247223259238511e-07, 'on_policy_loss': -0.0074, 'epoch': 0.08}
[OPSD kl] step 78: mean per-token KL(T||S)=0.1363 over 23425 tokens, frac above tau=0.411
{'loss': -0.0087, 'grad_norm': 0.05460890009999275, 'learning_rate': 5.24612469060774e-07, 'on_policy_loss': -0.0087, 'epoch': 0.09}

[stdout]
{'loss': -0.0079, 'grad_norm': 0.051960911601781845, 'learning_rate': 7.322330470336314e-07, 'on_policy_loss': -0.0079, 'epoch': 0.08}
{'loss': -0.0074, 'grad_norm': 0.0527726486325264, 'learning_rate': 6.247223259238511e-07, 'on_policy_loss': -0.0074, 'epoch': 0.08}
[OPSD kl] step 78: mean per-token KL(T||S)=0.1363 over 23425 tokens, frac above tau=0.411
{'loss': -0.0087, 'grad_norm': 0.05460890009999275, 'learning_rate': 5.24612469060774e-07, 'on_policy_loss': -0.0087, 'epoch': 0.09}
[metadata] {"noOutputExpected": false}

#103

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashCheck cosine run and eval

args

{
  "command": "sleep 115; sleep 115; sleep 115; sleep 115; sleep 115; python - <<'EOF'\nimport os\nfor f in ['/workspace/runs_COS.log','/workspace/eval_COS.log']:\n    if not os.path.exists(f): print(f,\"MISSING\"); continue\n    t=open(f,errors='ignore').read()\n    lines=[l.split('\\r')[-1] for l in t.split('\\n') if l.strip()]\n    sel=[l for l in lines if any(k in l for k in ['train_runtime','FINAL','Traceback'])]\n    prog=[l for l in lines if 'Processed prompts' in l]\n    print(\"==\",os.path.basename(f)); print('\\n'.join(l[:160] for l in sel[-2:]) or \"(none)\")\n    if prog: print(\"  prog:\",prog[-1][:90])\nEOF\ncat /workspace/.timer/remaining_secs",
  "description": "Check cosine run and eval",
  "timeout": 700000
}

Bash result

== runs_COS.log
{'train_runtime': 902.7347, 'train_samples_per_second': 3.545, 'train_steps_per_second': 0.111, 'train_loss': -0.004397831559181213, 'epoch': 0.11}
== eval_COS.log
(none)
  prog: Processed prompts:  29%|██▊       | 24/84 [05:33<11:32, 11.53s/it, est. speed input: 5.39 
13886

[stdout]
== runs_COS.log
{'train_runtime': 902.7347, 'train_samples_per_second': 3.545, 'train_steps_per_second': 0.111, 'train_loss': -0.004397831559181213, 'epoch': 0.11}
== eval_COS.log
(none)
  prog: Processed prompts:  29%|██▊       | 24/84 [05:33<11:32, 11.53s/it, est. speed input: 5.39 
13886
[metadata] {"noOutputExpected": false}

#104

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashCheck cosine eval result

args

{
  "command": "sleep 115; sleep 115; sleep 115; sleep 115; sleep 115; sleep 115; python - <<'EOF'\nt=open('/workspace/eval_COS.log',errors='ignore').read()\nlines=[l.split('\\r')[-1] for l in t.split('\\n') if l.strip()]\nsel=[l for l in lines if 'FINAL' in l or 'Traceback' in l]\nprint('\\n'.join(l[:170] for l in sel[-2:]) or \"(none yet)\")\nprog=[l for l in lines if 'Processed prompts' in l]\nprint(\"prog:\",prog[-1][:90] if prog else \"\")\nEOF",
  "description": "Check cosine eval result",
  "timeout": 800000
}

Bash result · error

Exit code 143
Command timed out after 10m 0s

[error] tool reported failure

#105

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashCheck cosine eval result

args

{
  "command": "python - <<'EOF'\nt=open('/workspace/eval_COS.log',errors='ignore').read()\nlines=[l.split('\\r')[-1] for l in t.split('\\n') if l.strip()]\nsel=[l for l in lines if 'FINAL' in l or 'Traceback' in l]\nprint('\\n'.join(l[:170] for l in sel[-2:]) or \"(none yet)\")\nprog=[l for l in lines if 'Processed prompts' in l]\nprint(\"prog:\",prog[-1][:90] if prog else \"\")\nEOF\ncat /workspace/.timer/remaining_secs",
  "description": "Check cosine eval result"
}

Bash result

(none yet)
prog: Processed prompts:  71%|███████▏  | 60/84 [16:01<03:39,  9.16s/it, est. speed input: 6.36 
13275

[stdout]
(none yet)
prog: Processed prompts:  71%|███████▏  | 60/84 [16:01<03:39,  9.16s/it, est. speed input: 6.36 
13275
[metadata] {"noOutputExpected": false}

#106

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashCheck cosine eval result

args

{
  "command": "sleep 115; sleep 115; sleep 115; python - <<'EOF'\nt=open('/workspace/eval_COS.log',errors='ignore').read()\nlines=[l.split('\\r')[-1] for l in t.split('\\n') if l.strip()]\nsel=[l for l in lines if 'FINAL' in l or 'Traceback' in l]\nprint('\\n'.join(l[:170] for l in sel[-2:]) or \"(none yet)\")\nEOF",
  "description": "Check cosine eval result",
  "timeout": 400000
}

Bash result

(none yet)

[stdout]
(none yet)
[metadata] {"noOutputExpected": false}

#107

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashCheck cosine eval result

args

{
  "command": "sleep 115; sleep 115; python - <<'EOF'\nt=open('/workspace/eval_COS.log',errors='ignore').read()\nlines=[l.split('\\r')[-1] for l in t.split('\\n') if l.strip()]\nsel=[l for l in lines if 'FINAL' in l or 'Traceback' in l]\nprint('\\n'.join(l[:170] for l in sel[-2:]) or \"(none yet)\")\nprog=[l for l in lines if 'Processed prompts' in l]\nprint(\"prog:\",prog[-1][:100] if prog else \"\")\nEOF",
  "description": "Check cosine eval result",
  "timeout": 300000
}

Bash result

FINAL avg@12 accuracy: 55.83% (201/360 solutions correct on 30 problems)
prog: Processed prompts: 100%|██████████| 96/96 [24:23<00:00, 15.25s/it, est. speed input: 8.94 toks/s, ou

[stdout]
FINAL avg@12 accuracy: 55.83% (201/360 solutions correct on 30 problems)
prog: Processed prompts: 100%|██████████| 96/96 [24:23<00:00, 15.25s/it, est. speed input: 8.94 toks/s, ou
[metadata] {"noOutputExpected": false}

#108

narration

Cosine LR decay: 55.83 vs the 54.22 anchor (+1.6, ≈1.1σ for a single run). That's my one positive result, and it's exactly the overshoot prediction. It needs replication.

From here on I'll run it precisely as the scorer does — no env vars at all, so the code defaults plus recipe.env are what's tested. Chaining two replicates at seed 42:

toolBashTwo cosine replicates at seed 42, exactly as the scorer runs it

args

{
  "command": "cd /workspace && SEED=42 OUT_DIR=/workspace/runs/COS2 bash /workspace/train_capped.sh > /workspace/runs_COS2.log 2>&1 && bash /workspace/dev_eval.sh /workspace/runs/COS2/capped_seed42/checkpoint-100 > /workspace/eval_COS2.log 2>&1; echo \"R2 done\"; SEED=42 OUT_DIR=/workspace/runs/COS3 bash /workspace/train_capped.sh > /workspace/runs_COS3.log 2>&1 && bash /workspace/dev_eval.sh /workspace/runs/COS3/capped_seed42/checkpoint-100 > /workspace/eval_COS3.log 2>&1; echo \"R3 done\"",
  "description": "Two cosine replicates at seed 42, exactly as the scorer runs it",
  "timeout": 3000000,
  "run_in_background": true
}

Bash result

Command running in background with ID: bz5eou72u. Output is being written to: /tmp/claude-0/-workspace/5fd2f3dc-5928-49f9-b3bb-a6ebc92f09aa/tasks/bz5eou72u.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace/repo; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bz5eou72u", "backgroundCwdHint": "Session cwd remains /workspace/repo; directory changes made by the backgrounded command do not apply to subsequent commands."}

#109

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashConfirm scorer-identical config in replicate

args

{
  "command": "sleep 115; python - <<'EOF'\nt=open('/workspace/runs_COS2.log',errors='ignore').read()\nlines=[l.split('\\r')[-1] for l in t.split('\\n')]\nsel=[l for l in lines if any(k in l for k in ['ignoring','padding_side','clip mode','OPSD data','Traceback',\"'loss'\"])]\nprint('\\n'.join(l[:170] for l in sel[-6:]))\nEOF",
  "description": "Confirm scorer-identical config in replicate",
  "timeout": 200000
}

Bash result

[OPSD] jsd clip mode: entry (tau=0.05)
[OPSD] jsd clip mode: entry (tau=0.05)
[OPSD] jsd clip mode: entry (tau=0.05)
{'loss': 0.0102, 'grad_norm': 0.16685977578163147, 'learning_rate': 4.998766400914329e-06, 'on_policy_loss': 0.0102, 'epoch': 0.0}
{'loss': 0.0086, 'grad_norm': 0.13990351557731628, 'learning_rate': 4.9889049115077e-06, 'on_policy_loss': 0.0086, 'epoch': 0.0}
{'loss': 0.0083, 'grad_norm': 0.14014355838298798, 'learning_rate': 4.9692208514878445e-06, 'on_policy_loss': 0.0083, 'epoch': 0.01}

[stdout]
[OPSD] jsd clip mode: entry (tau=0.05)
[OPSD] jsd clip mode: entry (tau=0.05)
[OPSD] jsd clip mode: entry (tau=0.05)
{'loss': 0.0102, 'grad_norm': 0.16685977578163147, 'learning_rate': 4.998766400914329e-06, 'on_policy_loss': 0.0102, 'epoch': 0.0}
{'loss': 0.0086, 'grad_norm': 0.13990351557731628, 'learning_rate': 4.9889049115077e-06, 'on_policy_loss': 0.0086, 'epoch': 0.0}
{'loss': 0.0083, 'grad_norm': 0.14014355838298798, 'learning_rate': 4.9692208514878445e-06, 'on_policy_loss': 0.0083, 'epoch': 0.01}
[metadata] {"noOutputExpected": false}

#110

narration

Confirmed scorer-identical (no "ignoring non-whitelisted key" warning, cosine active). Waiting on the two replicates.

toolBashPoll replicate progress

args

{
  "command": "sleep 590; python - <<'EOF'\nimport os\nfor f in ['/workspace/eval_COS2.log','/workspace/eval_COS3.log']:\n    if not os.path.exists(f): print(os.path.basename(f),\": not started\"); continue\n    t=open(f,errors='ignore').read()\n    lines=[l.split('\\r')[-1] for l in t.split('\\n') if l.strip()]\n    fin=[l for l in lines if 'FINAL' in l]\n    prog=[l for l in lines if 'Processed prompts' in l]\n    print(os.path.basename(f), fin[-1][:70] if fin else (prog[-1][:60] if prog else \"starting\"))\nEOF",
  "description": "Poll replicate progress",
  "timeout": 600000
}

Bash result

eval_COS2.log : not started
eval_COS3.log : not started

[stdout]
eval_COS2.log : not started
eval_COS3.log : not started
[metadata] {"noOutputExpected": false}

#111

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPoll replicate progress

args

{
  "command": "sleep 590; python - <<'EOF'\nimport os\nfor f in ['/workspace/eval_COS2.log','/workspace/eval_COS3.log']:\n    if not os.path.exists(f): print(os.path.basename(f),\": not started\"); continue\n    t=open(f,errors='ignore').read()\n    lines=[l.split('\\r')[-1] for l in t.split('\\n') if l.strip()]\n    fin=[l for l in lines if 'FINAL' in l]\n    prog=[l for l in lines if 'Processed prompts' in l]\n    print(os.path.basename(f), fin[-1][:70] if fin else (prog[-1][:60] if prog else \"starting\"))\nEOF\ncat /workspace/.timer/remaining_secs",
  "description": "Poll replicate progress",
  "timeout": 600000
}

Bash result

eval_COS2.log Processed prompts:  25%|██▌       | 24/96 [04:34<11:21,  9.4
eval_COS3.log : not started
11342

[stdout]
eval_COS2.log Processed prompts:  25%|██▌       | 24/96 [04:34<11:21,  9.4
eval_COS3.log : not started
11342
[metadata] {"noOutputExpected": false}

#112

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPoll for R2 result

args

{
  "command": "sleep 590; python - <<'EOF'\nimport os\nfor f in ['/workspace/eval_COS2.log','/workspace/eval_COS3.log']:\n    if not os.path.exists(f): print(os.path.basename(f),\": not started\"); continue\n    t=open(f,errors='ignore').read()\n    lines=[l.split('\\r')[-1] for l in t.split('\\n') if l.strip()]\n    fin=[l for l in lines if 'FINAL' in l]\n    prog=[l for l in lines if 'Processed prompts' in l]\n    print(os.path.basename(f), fin[-1][:70] if fin else (prog[-1][:60] if prog else \"starting\"))\nEOF",
  "description": "Poll for R2 result",
  "timeout": 600000
}

Bash result

eval_COS2.log Processed prompts:  43%|████▎     | 36/84 [14:55<14:07, 17.6
eval_COS3.log : not started

[stdout]
eval_COS2.log Processed prompts:  43%|████▎     | 36/84 [14:55<14:07, 17.6
eval_COS3.log : not started
[metadata] {"noOutputExpected": false}

#113

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPoll for R2 result

args

{
  "command": "sleep 590; python - <<'EOF'\nimport os\nfor f in ['/workspace/eval_COS2.log','/workspace/eval_COS3.log']:\n    if not os.path.exists(f): print(os.path.basename(f),\": not started\"); continue\n    t=open(f,errors='ignore').read()\n    lines=[l.split('\\r')[-1] for l in t.split('\\n') if l.strip()]\n    fin=[l for l in lines if 'FINAL' in l]\n    prog=[l for l in lines if 'Processed prompts' in l]\n    print(os.path.basename(f), fin[-1][:70] if fin else (prog[-1][:60] if prog else \"starting\"))\nEOF",
  "description": "Poll for R2 result",
  "timeout": 600000
}

Bash result

eval_COS2.log FINAL avg@12 accuracy: 54.72% (197/360 solutions correct on 30 problem
eval_COS3.log : not started

[stdout]
eval_COS2.log FINAL avg@12 accuracy: 54.72% (197/360 solutions correct on 30 problem
eval_COS3.log : not started
[metadata] {"noOutputExpected": false}

#114

narration

Replicate 2: 54.72. Cosine so far: 55.83, 54.72 (mean 55.28). Waiting on replicate 3.

toolBashPoll R3

args

{
  "command": "sleep 590; python - <<'EOF'\nimport os\nfor f in ['/workspace/eval_COS3.log']:\n    if not os.path.exists(f): print(\"not started\"); continue\n    t=open(f,errors='ignore').read()\n    lines=[l.split('\\r')[-1] for l in t.split('\\n') if l.strip()]\n    fin=[l for l in lines if 'FINAL' in l]\n    prog=[l for l in lines if 'Processed prompts' in l]\n    print(fin[-1][:70] if fin else (prog[-1][:60] if prog else \"starting\"))\nEOF\ncat /workspace/.timer/remaining_secs",
  "description": "Poll R3",
  "timeout": 600000
}

Bash result

not started
9519

[stdout]
not started
9519
[metadata] {"noOutputExpected": false}

#115

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPoll R3

args

{
  "command": "sleep 590; python - <<'EOF'\nimport os\nf='/workspace/eval_COS3.log'\nif not os.path.exists(f): print(\"eval not started\")\nelse:\n    t=open(f,errors='ignore').read()\n    lines=[l.split('\\r')[-1] for l in t.split('\\n') if l.strip()]\n    fin=[l for l in lines if 'FINAL' in l]\n    prog=[l for l in lines if 'Processed prompts' in l]\n    print(fin[-1][:70] if fin else (prog[-1][:60] if prog else \"starting\"))\nEOF",
  "description": "Poll R3",
  "timeout": 600000
}

Bash result

Processed prompts:  25%|██▌       | 24/96 [04:14<11:23,  9.4

[stdout]
Processed prompts:  25%|██▌       | 24/96 [04:14<11:23,  9.4
[metadata] {"noOutputExpected": false}

#116

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPoll R3

args

{
  "command": "sleep 590; python - <<'EOF'\nt=open('/workspace/eval_COS3.log',errors='ignore').read()\nlines=[l.split('\\r')[-1] for l in t.split('\\n') if l.strip()]\nfin=[l for l in lines if 'FINAL' in l]\nprog=[l for l in lines if 'Processed prompts' in l]\nprint(fin[-1][:70] if fin else (prog[-1][:60] if prog else \"starting\"))\nEOF\ncat /workspace/.timer/remaining_secs",
  "description": "Poll R3",
  "timeout": 600000
}

Bash result

Processed prompts:  88%|████████▊ | 84/96 [14:36<01:57,  9.7
8307

[stdout]
Processed prompts:  88%|████████▊ | 84/96 [14:36<01:57,  9.7
8307
[metadata] {"noOutputExpected": false}

#117

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashGet R3 result

args

{
  "command": "sleep 300; python - <<'EOF'\nt=open('/workspace/eval_COS3.log',errors='ignore').read()\nlines=[l.split('\\r')[-1] for l in t.split('\\n') if l.strip()]\nfin=[l for l in lines if 'FINAL' in l]\nprint(fin[-1][:70] if fin else \"not yet\")\nEOF",
  "description": "Get R3 result",
  "timeout": 400000
}

Bash result

not yet

[stdout]
not yet
[metadata] {"noOutputExpected": false}

#118

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashGet R3 result

args

{
  "command": "sleep 240; python - <<'EOF'\nt=open('/workspace/eval_COS3.log',errors='ignore').read()\nlines=[l.split('\\r')[-1] for l in t.split('\\n') if l.strip()]\nfin=[l for l in lines if 'FINAL' in l]\nprog=[l for l in lines if 'Processed prompts' in l]\nprint(fin[-1][:70] if fin else prog[-1][:70])\nEOF\nnvidia-smi --query-gpu=memory.used --format=csv,noheader",
  "description": "Get R3 result",
  "timeout": 400000
}

Bash result

FINAL avg@12 accuracy: 56.67% (204/360 solutions correct on 30 problem
4 MiB
4 MiB
4 MiB
4 MiB

[stdout]
FINAL avg@12 accuracy: 56.67% (204/360 solutions correct on 30 problem
4 MiB
4 MiB
4 MiB
4 MiB
[metadata] {"noOutputExpected": false}

#119

narration

Replicate 3: 56.67. Cosine at seed 42: 55.83, 54.72, 56.67 → mean 55.74 (sd 0.98) vs the 54.22 anchor, i.e. +1.5 at ~2σ of the mean. That's my shippable result.

~2.1 h left, so two cycles. Spending one on the remaining untested idea — an exploratory-trace teacher instead of the terse reference solution, which is the paper's own diagnosis of what hurts:

toolBashCycle: cosine + exploratory-trace teacher context

args

{
  "command": "cd /workspace && OPSD_TEACHER_CONTEXT=cot SEED=42 OUT_DIR=/workspace/runs/COT bash /workspace/train_capped.sh > /workspace/runs_COT.log 2>&1 && echo TRAIN_OK && bash /workspace/dev_eval.sh /workspace/runs/COT/capped_seed42/checkpoint-100 > /workspace/eval_COT.log 2>&1; echo \"EXIT=$?\"",
  "description": "Cycle: cosine + exploratory-trace teacher context",
  "timeout": 3000000,
  "run_in_background": true
}

Bash result

Command running in background with ID: bnt65wehy. Output is being written to: /tmp/claude-0/-workspace/5fd2f3dc-5928-49f9-b3bb-a6ebc92f09aa/tasks/bnt65wehy.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace/repo; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bnt65wehy", "backgroundCwdHint": "Session cwd remains /workspace/repo; directory changes made by the backgrounded command do not apply to subsequent commands."}

#120

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashMonitor CoT-teacher run for OOM

args

{
  "command": "sleep 200; python - <<'EOF'\nt=open('/workspace/runs_COT.log',errors='ignore').read()\nlines=[l.split('\\r')[-1] for l in t.split('\\n')]\nsel=[l for l in lines if any(k in l for k in ['OPSD align','OPSD kl','Traceback','OutOfMemory',\"'loss'\",'Error'])]\nprint('\\n'.join(l[:230] for l in sel[-8:]))\nEOF\nnvidia-smi --query-gpu=memory.used --format=csv,noheader; cat /workspace/.timer/remaining_secs",
  "description": "Monitor CoT-teacher run for OOM",
  "timeout": 300000
}

Bash result

[OPSD kl] step 6: mean per-token KL(T||S)=0.1798 over 23921 tokens, frac above tau=0.255
{'loss': 0.0058, 'grad_norm': 0.10297249257564545, 'learning_rate': 4.939791904846869e-06, 'on_policy_loss': 0.0058, 'epoch': 0.01}
[OPSD kl] step 9: mean per-token KL(T||S)=0.1518 over 21248 tokens, frac above tau=0.237
{'loss': 0.0041, 'grad_norm': 0.08360190689563751, 'learning_rate': 4.900734214192358e-06, 'on_policy_loss': 0.0041, 'epoch': 0.01}
{'loss': 0.0022, 'grad_norm': 0.091121144592762, 'learning_rate': 4.852201922385564e-06, 'on_policy_loss': 0.0022, 'epoch': 0.01}
[OPSD kl] step 13: mean per-token KL(T||S)=0.1860 over 20296 tokens, frac above tau=0.246
{'loss': 0.0011, 'grad_norm': 0.06317288428544998, 'learning_rate': 4.794386564209953e-06, 'on_policy_loss': 0.0011, 'epoch': 0.02}
{'loss': 0.002, 'grad_norm': 0.06549064069986343, 'learning_rate': 4.72751631047092e-06, 'on_policy_loss': 0.002, 'epoch': 0.02}
67933 MiB
67147 MiB
66569 MiB
68169 MiB
7515

[stdout]
[OPSD kl] step 6: mean per-token KL(T||S)=0.1798 over 23921 tokens, frac above tau=0.255
{'loss': 0.0058, 'grad_norm': 0.10297249257564545, 'learning_rate': 4.939791904846869e-06, 'on_policy_loss': 0.0058, 'epoch': 0.01}
[OPSD kl] step 9: mean per-token KL(T||S)=0.1518 over 21248 tokens, frac above tau=0.237
{'loss': 0.0041, 'grad_norm': 0.08360190689563751, 'learning_rate': 4.900734214192358e-06, 'on_policy_loss': 0.0041, 'epoch': 0.01}
{'loss': 0.0022, 'grad_norm': 0.091121144592762, 'learning_rate': 4.852201922385564e-06, 'on_policy_loss': 0.0022, 'epoch': 0.01}
[OPSD kl] step 13: mean per-token KL(T||S)=0.1860 over 20296 tokens, frac above tau=0.246
{'loss': 0.0011, 'grad_norm': 0.06317288428544998, 'learning_rate': 4.794386564209953e-06, 'on_policy_loss': 0.0011, 'epoch': 0.02}
{'loss': 0.002, 'grad_norm': 0.06549064069986343, 'learning_rate': 4.72751631047092e-06, 'on_policy_loss': 0.002, 'epoch': 0.02}
67933 MiB
67147 MiB
66569 MiB
68169 MiB
7515
[metadata] {"noOutputExpected": false}

#121

narration

No OOM (67 GB / 80 GB), training healthy. Waiting.

toolBashMonitor CoT run

args

{
  "command": "sleep 590; python - <<'EOF'\nimport os\nt=open('/workspace/runs_COT.log',errors='ignore').read()\nlines=[l.split('\\r')[-1] for l in t.split('\\n')]\nsel=[l for l in lines if \"'loss'\" in l or 'train_runtime' in l or 'Traceback' in l]\nprint('\\n'.join(l[:150] for l in sel[-2:]))\nprint(\"eval:\", os.path.exists('/workspace/eval_COT.log'))\nEOF",
  "description": "Monitor CoT run",
  "timeout": 600000
}

Bash result

{'loss': -0.0055, 'grad_norm': 0.04519413411617279, 'learning_rate': 6.247223259238511e-07, 'on_policy_loss': -0.0055, 'epoch': 0.08}
{'loss': -0.006, 'grad_norm': 0.03662177920341492, 'learning_rate': 5.24612469060774e-07, 'on_policy_loss': -0.006, 'epoch': 0.09}
eval: False

[stdout]
{'loss': -0.0055, 'grad_norm': 0.04519413411617279, 'learning_rate': 6.247223259238511e-07, 'on_policy_loss': -0.0055, 'epoch': 0.08}
{'loss': -0.006, 'grad_norm': 0.03662177920341492, 'learning_rate': 5.24612469060774e-07, 'on_policy_loss': -0.006, 'epoch': 0.09}
eval: False
[metadata] {"noOutputExpected": false}

#122

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashMonitor CoT eval

args

{
  "command": "sleep 590; python - <<'EOF'\nimport os\nf='/workspace/eval_COT.log'\nif not os.path.exists(f):\n    t=open('/workspace/runs_COT.log',errors='ignore').read()\n    sel=[l.split('\\r')[-1] for l in t.split('\\n') if 'train_runtime' in l]\n    print(\"train:\", sel[-1][:120] if sel else \"running\")\nelse:\n    t=open(f,errors='ignore').read()\n    lines=[l.split('\\r')[-1] for l in t.split('\\n') if l.strip()]\n    fin=[l for l in lines if 'FINAL' in l]; prog=[l for l in lines if 'Processed prompts' in l]\n    print(fin[-1][:70] if fin else (prog[-1][:60] if prog else \"starting\"))\nEOF\ncat /workspace/.timer/remaining_secs",
  "description": "Monitor CoT eval",
  "timeout": 600000
}

Bash result

Processed prompts:  25%|██▌       | 24/96 [03:53<10:02,  8.3
6323

[stdout]
Processed prompts:  25%|██▌       | 24/96 [03:53<10:02,  8.3
6323
[metadata] {"noOutputExpected": false}

#123

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashGet CoT result

args

{
  "command": "sleep 590; python - <<'EOF'\nt=open('/workspace/eval_COT.log',errors='ignore').read()\nlines=[l.split('\\r')[-1] for l in t.split('\\n') if l.strip()]\nfin=[l for l in lines if 'FINAL' in l]; prog=[l for l in lines if 'Processed prompts' in l]\nprint(fin[-1][:70] if fin else (prog[-1][:60] if prog else \"starting\"))\nEOF",
  "description": "Get CoT result",
  "timeout": 600000
}

Bash result

Processed prompts: 100%|██████████| 96/96 [14:52<00:00,  9.3

[stdout]
Processed prompts: 100%|██████████| 96/96 [14:52<00:00,  9.3
[metadata] {"noOutputExpected": false}

#124

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashGet CoT result

args

{
  "command": "sleep 420; python - <<'EOF'\nt=open('/workspace/eval_COT.log',errors='ignore').read()\nlines=[l.split('\\r')[-1] for l in t.split('\\n') if l.strip()]\nfin=[l for l in lines if 'FINAL' in l]; prog=[l for l in lines if 'Processed prompts' in l]\nprint(fin[-1][:70] if fin else (prog[-1][:60] if prog else \"starting\"))\nEOF\ncat /workspace/.timer/remaining_secs",
  "description": "Get CoT result",
  "timeout": 500000
}

Bash result

FINAL avg@12 accuracy: 54.44% (196/360 solutions correct on 30 problem
5282

[stdout]
FINAL avg@12 accuracy: 54.44% (196/360 solutions correct on 30 problem
5282
[metadata] {"noOutputExpected": false}

#125

narration

Exploratory-trace teacher: 54.44 — below the cosine mean, so I'm discarding it (default stays the concise solution). ~88 min left; spending one final cycle on a 4th replicate of the candidate I'm shipping.

toolBashVerify submission state before final replicate

args

{
  "command": "git --no-pager diff --stat | tail -3; python - <<'EOF'\nimport re\nfor f,keys in [('/workspace/repo/data_collator.py',['STUDENT_PROMPT_STYLE = ','TEACHER_CONTEXT = ','PAD_SIDE = ']),\n               ('/workspace/repo/opsd_trainer.py',['self.jsd_clip_mode = ']),\n               ('/workspace/repo/opsd_train.py',['data_select = '])]:\n    s=open(f).read()\n    for k in keys:\n        i=s.find(k); print(f.split('/')[-1], '|', s[i:s.find('\\n',i)].strip())\nEOF\necho \"--- recipe.env active keys:\"; grep -v '^#' /workspace/submission/recipe.env 2>/dev/null | sed '/^$/d' || python -c \"\nprint([l.strip() for l in open('/workspace/submission/recipe.env') if l.strip() and not l.strip().startswith('#')])\"",
  "description": "Verify submission state before final replicate"
}

Bash result

opsd_train.py    |  31 ++++++++++++++++
 opsd_trainer.py  | 106 ++++++++++++++++++++++++++++++++++++++++++++++++++-----
 4 files changed, 186 insertions(+), 15 deletions(-)
data_collator.py | STUDENT_PROMPT_STYLE = os.environ.get("OPSD_STUDENT_PROMPT", "paper")
data_collator.py | TEACHER_CONTEXT = os.environ.get("OPSD_TEACHER_CONTEXT", "solution")
data_collator.py | PAD_SIDE = os.environ.get("OPSD_PAD_SIDE", "right")
opsd_trainer.py | self.jsd_clip_mode = os.environ.get("OPSD_CLIP_MODE", "entry")
opsd_train.py | data_select = os.environ.get("OPSD_DATA_SELECT", "none")
--- recipe.env active keys:
lr_scheduler_type=cosine

[stdout]
 opsd_train.py    |  31 ++++++++++++++++
 opsd_trainer.py  | 106 ++++++++++++++++++++++++++++++++++++++++++++++++++-----
 4 files changed, 186 insertions(+), 15 deletions(-)
data_collator.py | STUDENT_PROMPT_STYLE = os.environ.get("OPSD_STUDENT_PROMPT", "paper")
data_collator.py | TEACHER_CONTEXT = os.environ.get("OPSD_TEACHER_CONTEXT", "solution")
data_collator.py | PAD_SIDE = os.environ.get("OPSD_PAD_SIDE", "right")
opsd_trainer.py | self.jsd_clip_mode = os.environ.get("OPSD_CLIP_MODE", "entry")
opsd_train.py | data_select = os.environ.get("OPSD_DATA_SELECT", "none")
--- recipe.env active keys:
lr_scheduler_type=cosine
[metadata] {"noOutputExpected": false}